Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It implies a read operation via 'Returns' and states the detection criterion, but does not disclose data freshness, whether spreads account for fees or liquidity, cross-platform matching behavior, or any output shape.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence with no wasted words, making it easy to scan. Its brevity is efficient, though additional context would be useful elsewhere.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a cross-platform arbitrage detector with no output schema and no annotations, the description is too thin. It does not explain how opportunities are computed, whether both platforms are always queried, what fields are returned, or any caveats about stale or incomplete comparisons.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema already documents limit, min_spread, and notification_message with defaults and ranges. The description adds no parameter-level meaning, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource ('Returns markets') and names the detection criteria: mispriced outcomes and cross-platform Polymarket vs Kalshi inconsistencies. This distinguishes it from generic market-data siblings, though it does not explicitly contrast with tools like get_market_odds or get_market_movers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used to find arbitrage opportunities but gives no explicit when-to-use, when-not-to-use, or alternative-tool guidance. An agent has to infer its role relative to the numerous sibling market tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.